CourseProfile (ATLAS), EECS 650. DC and AC circuit models for diodes, bipolar junction transistors and field-effect transistors; small-signal and piecewise analysis of nonlinear circuits; analysis and design of single-stage and multi-stage transistor amplifiers: gain, biasing and frequency response; op-amp based filter design; non-ideal op-amps. Review of single variable systems and extensions to multivariable systems. You should understand basic discrete mathematics including recursion relations, big-Oh Optimum receivers in Gaussian noise. Prerequisite: EECS 281 and (MATH 214 or MATH 217 or MATH 296 or MATH 417). In depth understanding of the device physics and working principle of some basic IC components: metal-semiconductor junctions, P-N junctions, metal-oxide-semiconductor junctions, MOSFETs and BJTs. Prerequisite: EECS 281. The courses are divided into the 12 research areas a graduate student can major in. Prerequisite: EECS 489. Topics include: Java syntax and semantics, object-oriented design, exception handling, graphical user interfaces, mobile-application development, asynchronous programming, and unit testing. Prerequisite: EECS 330 or Physics 438. Advisory: EECS 370. CourseProfile (ATLAS), EECS 373. Introduction to Adaptive Systems Project requires the design and set-up of a practical optical system. Numerical Methods in Electromagnetics  CourseProfile (ATLAS), EECS 620. Prerequisite: EECS 330. CourseProfile (ATLAS), EECS 538 (APPPHYS 550) (PHYSICS 650). Instruction Mode: Online – Synchronous Integrated circuit fabrication overview, relationships between processing choices and device performance characteristics. Case studies taken from current microprocessors. Aperture antennas: slot, Babinet’s principle. Lecture and laboratory. Students will learn to… Implement small-to-medium sized video games using the industry-standard Unity3D Game Engine. Prerequisite: graduate standing. Instruction Mode: Online – Synchronous, In-Person – Synchronous Software Development for Accessibility Lecture, seminar, or laboratory. Shared-memory coherence and consistency. (3 credits) Instruction Mode:  DC motors. Hybrid system modeling formalisms, specifications (automata theory, temporal logics), verification (barrier certificates, reachable sets, abstraction-based methods) and control synthesis. The limiting case of electro- and magneto-statics. Time-varying fields: Faraday’s Law and displacement current. Basic concepts of probability theory. Prerequisite: EECS 330 (“C” or better) or graduate standing. (3 credits) Minimum grade requirement of C- for enforced prerequisites. CourseProfile (ATLAS), EECS 570. Design and analysis of mechanisms for problems motivated by areas such as electronic commerce, social computing, social choice, and information elicitation. Each student must take (simultaneously) Tech Comm 496 (2 cr.) Testing and debugging. (4 credits) CourseProfile (ATLAS), EECS 541 (APPPHYS 541). CourseProfile (ATLAS), EECS 544. Projects in chip design. Prerequisite: EECS 376 or graduate standing. (4 credits) Eligibility is limited to students who have a concentration GPA of 3.5 or better. CourseProfile (ATLAS), EECS 595 (LING 541) (SI 561). Prerequisite: EECS 281 or EECS 478 or graduate standing. Laboratory involves CAD-based design implemented on an FPGA including elementary interfacing. CourseProfile (ATLAS), EECS 285. EECS Course Descriptions. CourseProfile (ATLAS), EECS 398. Electronic Sensing Systems CMOS circuit delay and power analysis. CourseProfile (ATLAS), EECS 399. Computational Data Science and Machine Learning  Prerequisite: EECS 414. Resolution limitations. Instruction Mode: Online – Synchronous Robust and reliable design  techniques. CourseProfile (ATLAS), EECS 560 (AEROSP 550) (CEE 571) (MECHENG 564). Lab projects on CAD software development. CourseProfile (ATLAS), EECS 670. Not open to CE or EE students. (3 credits) Grad Course List. Quadratic mean calculus, including stochastic integrals and representations, wide-sense stationary processes (filtering, white noise, sampling, time averages, moving averages, autoregression). Theory of image formation and Fourier transformation by lenses. Instruction Mode: Online – Synchronous DC/DC converter design for PCBs. Instruction Mode: Online – Synchronous Prerequisite: EECS 301 or MATH 425 or STATS 250 or STATS 412 or STATS 426 or IOE 265 or graduate standing. CourseProfile (ATLAS), EECS 765. Foundations of Artificial Intelligence CourseProfile (ATLAS), EECS 517 (NERS 578). Mobile Computing The reason is simple. Mathematical representations: state equations, transfer functions, impulse response, matrix fraction and polynomial descriptions. Prerequisite: EECS 484 or permission of instructor. Theory of digital modulation and coding. Major CMOS scaling challenges. The Regents of the University of Michigan, Michigan Engineering | College Administration, 1221 Beal Avenue, Ann Arbor, MI 48109-2102, Safety Information | Privacy Policy | CourseProfile (ATLAS), EECS 627. Transmission-line theory, microstrip and coplanar lines, S-parameters, signal-flow graphs, matching networks, directional couplers, low-pass and band-pass filters, diode detectors. Instruction Mode: Online – Synchronous Design, development, and application of digital games. Real world projects, usually in partnership with hospitals for specific disabled clients. Instruction Mode: Online – Synchronous General principles of magnetohydrodynamics; theory of the expanding atmospheres; properties of solar wind, interaction of solar wind with the magneto-sphere of the Earth and other planets; bow shock and magnetotail, trapped particles, auroras. General properties and design of linear and nonlinear solid state microwave circuits including: amplifier gain blocks, low-noise, broadband and power amplifiers, oscillators, mixer and multiplier circuits, packaging, system implementation for wireless communication. (3 credits) Programs and automata that “learn” by adapting to their environment; programs that utilize genetic algorithms for learning. Prerequisite: EECS 530 and graduate standing. Instruction Mode: Online – Synchronous The course lays a framework for the extraction of useful information from images. Problems are placed in the context of real electricity markets. Individual study of selected advanced topics in electrical engineering and computer science. CourseProfile (ATLAS), EECS 559. (4 credits) Emphasizes the application of AI techniques. Theory and Practice of Data Compression CourseProfile (ATLAS), EECS 523. Microstrip antennas. Prerequisite: graduate standing, permission of instructor. (3 credits) Prerequisite: EECS 330 or EECS 334 or permission of instructor or graduate standing. This is a 1-credit hour seminar designed to teach students the essentials of using a computer effectively for EECS students. CourseProfile (ATLAS), EECS 568 (NAVARCH 568). Laboratory experience with electrical signals and circuits. Basic Concepts of voltage and current; Kirchhoff’s voltage and current laws; Ohm’s law; voltage and current sources; Thevenin and Norton equivalent circuits; DC and low frequency active circuits using operational amplifiers, diodes, and transistors; small signal analysis; energy and power. Instruction Mode: Online – Asynchronous Team-based, user-centered design and development of complex software systems incorporating effective design strategies and project management methodologies. CourseProfile (ATLAS), EECS 587. Design verification: simulation, formal techniques, and post-silicon validation. Artificial intelligence systems, such as NETL and SOAR, are examined for their impact upon machine learning and cognitive science. Linear Systems Theory Electrical Engineering Systems Design II Prerequisite: EECS 200, at least 3 of 4 (215, 216, 230, 280), Co-requisite EECS: 4th of 4 (215, 216, 230, 280) Minimum grade of C required for enforced prerequisites. Estimation: linear and nonlinear minimum mean squared error estimation, and other strategies. Prerequisite: (EECS 203 or Math 465 or Math 565) and EECS 280. 3EE students are advised to take EECS 301 no later than the sixth semester. Applications, including RF MEMS, optical MEMS, bioMEMS, and microfluidics. Renewal and regenerative processes, Markov chains, random walk and run, branching processes, Markov jump processes, uniformization, reversibility and queuing applications. Instruction Mode: Online – Synchronous This course will provide basic knowledge to understand and apply principles of plasmonics. Algorithm development and effective programming, top-down analysis, structured programming, testing and program correctness. Emphasis is placed on performance trade-offs in protocol and architecture designs. Timing analysis and cycle time optimization. EECS 281). Instruction Mode: Online – Synchronous Crystal structure; Phonons; Introduction to Quantum Mechanics, Free electron Fermi gas; Low dimensional conductor; Electronic structure – Energy bands; Properties of semiconductors; Dielectrics response; Light absorption and emission; Magnetic effects; Superconductivity. Instruction Mode: Online – Synchronous Linguistic fundamentals of natural language processing (NLP), part of speech tagging, hidden Markov models, syntax and parsing, lexical semantics, compositional semantics, word sense disambiguation, machine translation. Emphasis on power and performance trade-offs. (4 credits). Students work in teams to develop and exhibit new games. Topics include mirrors, interferometers, modulators and propagation in waveguides and fibers. Instruction Mode: Online – Synchronous Evaluated Courses and UM Equivalencies (Expire 6 years after approval date) Subject # Title (Credits) Online UM Subject Catalog Credits Expires Backpack; ... Not equivalent to EECS 280, & does not have any programming experience as a prerequisite. (1-4 credits) Stochastic Processes (4 credits) Bandwidth and dimensionality. Motion planning including obstacle avoidance is also covered. (4 credits) (Not for graduate credit)  CourseProfile (ATLAS), EECS 583. (4 credits) CourseProfile (ATLAS), EECS 631. Classical Optics Computational Complexity Wire antennas: dipoles, loops and traveling-wave antennas. CourseProfile (ATLAS), EECS 367. Applications such as image deblurring, ranking of webpages, image segmentation and compression, social networks, circuit analysis, recommender systems and handwritten digit recognition. (3 credits) Computer-aided design algorithms. Minimum grade of “C”. Overview of modern optics with laboratory demonstrations. Introduction to advanced electromagnetics, communication systems, sensor systems, remote sensing and global navigation systems. CourseProfile (ATLAS), EECS 684. EECS 494 is the University of Michigan’s premiere game-development course. Advanced Compilers Practical Programming in Java The course covers wave reflection and transmission, dipoles, arrays, horn and patch antennas, waveguides, microstrip lines, resonators, and their applications in communication and radar systems. Topics covered include client/server protocols, security, information retrieval and search engines, scalable data processing, and fault tolerant systems. (4 credits) Instruction Mode: Online – Synchronous Digital transmission of information across discrete and analog channels. Robot Kinematics and Dynamics Grid Computing. Topics include representations of visual content (e.g., functions, points, graphs); visual invariance; mathematical and computational models of visual content; optimization methods for vision. Sufficient time to read and understand two 30-page research papers per week. Instruction Mode: Online – Synchronous Transduction techniques, including piezoelectric, electrothermal, and resonant techniques. Root locus, Nyquist and Bode plot-based techniques are outlined. Minimum grade of “C” required for enforced prerequisites. Instruction Mode: Online – Synchronous Coping with intractability. CourseProfile (ATLAS), EECS 550. Coherent and incoherent light. CourseProfile (ATLAS), EECS 599. Chemical, gas, and biological sensors, microfluidic and biomedical devices. Introduction and fundamentals of physical, optical and electrical properties of amorphous and microcrystalline semiconductor based devices: MIM structures, Schottky diodes, p-i-n junctions, heterojunctions, MIS structures, thin-film transistors, solar cells, threshold and memory switching devices and large area x-ray radiation detectors. Prerequisite: EECS 537. Prerequisite: (EECS 215 and EECS 216 and preceded or accompanied by EECS 320) or graduate standing. Advanced Computer Networks Analysis of micromachined capacitive, piezoresistive and thermal sensors/actuators and applications. Fundamentals of electromagnetic radiation and propagation (near earth, troposphere, ionosphere, indoor and urban); antenna parameters; practical antennas; link analysis; system noise; fading and multipath interference; applications. EECS 545: Machine Learning University of Michigan, Fall 2015. Networking. Equilibrium statistics of electrons and holes. Instead, mobile applications will be created using a novel visual programming environment. Pole placement/observer design. CourseProfile (ATLAS), EECS 578. Minimum grade of “C” for enforced prerequisite. CourseProfile (ATLAS), EECS 489. Prerequisite: EECS 330, Graduate Standing. User Interface Development Fundamental concepts and methods in data mining, and practical skills for mining massive, real data on distributed frameworks (e.g., Hadoop). Instruction Mode: Online – Synchronous Sampling, filtering, 2D Fourier transforms, interpolation, edge detection, enhancement, denoising, restoration, segmentation, random field models of images, Bayesian methods, wavelets and sparsity models. Prerequisites: Undergraduate linear algebra (e.g. Instruction Mode: Online – Synchronous Instruction Mode: Online – Synchronous, Hybrid – Synchronous Lectures and laboratory. Advanced graduate seminar devoted to discussing current research papers in artificial intelligence. (3 credits) Instruction Mode: Online – Synchronous Principles of modern medical imaging systems. Prerequisite: none. Presents concepts and hands-on experience for designing and writing programs using one or more programming languages currently important in solving real-world problems. (4 credits) [Fewer than two previous elections of EECS 203 (incl. CourseProfile (ATLAS), EECS 410 (ENGR 410) Patent Fundamentals for Engineers CourseProfile (ATLAS), EECS 638 (APPPHYS 609) (PHYSICS 542). CourseProfile (ATLAS), EECS 548 (SI 649). (3 credits) Principles of semiconducting lasers; gain-current relationships, radiation fields, optical confinement and transient effects. Topics include small-signal models; digital and analog control; switched, sampled-data, and averaged models; large signal considerations; distributed power; and tools for computer modeling and simulation. Introduction to numerical methods in electromagnetics including finite difference, finite element and integral equation methods for static, harmonic and time dependent fields; use of commercial software for analysis and design purposes; applications to open and shielded transmission lines, antennas, cavity resonances and scattering. FAQs Survey Feed-back Eval §3&4. CourseProfile (ATLAS), EECS 535. Electrical Engineering Systems Design II Instruction Mode: Online – Synchronous Instruction Mode: Hybrid – Synchronous, Online – Synchronous CourseProfile (ATLAS), EECS 484. CourseProfile (ATLAS), EECS 381. Required text: None. BioMEMS grades of W.I, VI, and AUD)] Electrical biophysics of nerve and muscle; electrical conduction in excitable tissue; quantitative models for nerve and muscle, including the Hodgkin Huxley equations; biopotential mapping, cardiac electrophysiology, and functional electrical stimulation; group projects. Power Semiconductor devices, inductors, capacitors. CourseProfile (ATLAS), EECS 435. Prerequisite: EECS 501. Structured data types, pointers, linked data structures, stacks, queues, arrays, records and trees. (1-4 credits) Control design concepts for linear multivariable systems. EECS 300. Instruction Mode: Online – Synchronous Integrated Microsystems Laboratory (3 credits) Instruction Mode: Online – Synchronous Special Topics in Theoretical Computer Science Adaptive Signal Processing Prerequisite: (EECS 270 and EECS 312) or graduate standing. Doing an independent study? Micromachining technologies such as laser machining and microdrilling, EDM, materials such as SiC and diamond. Design and Analysis of Algorithms Theory will cover: Bandstructure in quantum wells; effect of strain on bandstructure; transport theory; Monte Carlo methods for high field transport; excitons, optical absorption, luminescence and gain. (4 credits) Instruction Mode: In Person – Synchronous, Hybrid – Synchronous, Online – Synchronous (4 credits) (May not be taken if student has credit for or is currently enrolled in EECS 180, EECS 183, ENGR 101, ENGR 151, EECS 280 or EECS 282.) rackham grad umich eecs course list provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. Prerequisite: EECS 216 and EECS 301 or graduate standing. This course will examine computational models of human cognitive processes. Prerequisite: EECS 520 or EECS 540. (4 credits) Physics of operation of three terminal device structures important for high frequency analog or high speed digital applications. Optimization of systems described by Markov processes; dynamic programming under perfect and imperfect information, finite and infinite horizons. Instruction Mode: Online – Synchronous Dissertation/Candidate Layout Synthesis and Optimization Course goals include learning about important computational models of specific cognitive domains and evaluating the appropriateness and utility of different computational approaches to substantive problems in cognition. Fundamentals of the theory of computation and complexity theory. Emphasis on proven field-effect and bipolar-junction transistors, also including current and speculative nanoelectronic devices. CourseProfile (ATLAS), EECS 735. CourseProfile (ATLAS), EECS 534. M = Counts as a Major Area course automatically E = Counts as a Major Area course after approval by an advisor. Real time operating systems. Communicating sequential processes. The second half treats photons in semiconductors, including semi-conductor lasers, detectors and noise effects. CourseProfile (ATLAS), EECS 470. Prerequisite: EECS 216 or EECS 373 or graduate standing. MOS device scaling strategies, silicon-on-insulator, lightly-doped drain structures, on-chip interconnect parasitics and performance. CourseProfile (ATLAS), EECS 586. Emphasis on using these concepts in systems problems. CourseProfile (ATLAS), EECS 634 (APPPHYS 611) (Physics 611). Significant after hours lab time investment. Introduction to properties and behavior of electromagnetic energy as it pertains to naval applications of communication, radar, and electro-optics. CourseProfile (ATLAS), EECS 698. A hands-on, project based introduction to the principles of robotics and robot design. Instruction Mode: In Person – Asynchronous, Hybrid – Synchronous, Online – Synchronous Topics of current interest in electrical engineering and computer science. Instruction Mode: Online – Synchronous Emphasizes team-based development of large, complex, software systems using established software development methodology. Stability considerations, pole-zero cancellation, root locus techniques in feedback amplifiers.

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